Neuropsychological spatiotemporal pattern recognition

ABSTRACT

Systems and methods for identifying and analyzing neuropsychological flow patterns, include creating a knowledge base of neuropsychological flow patterns. The knowledge base is formed by obtaining signals from multiple research groups for particular behavioral processes, localizing sources of activity participating in the particular behavioral processes, identifying sets of patterns of brain activity for the behavioral processes and neuropsychologically analyzing the localized sources and the identified patterns for each of the research groups. The neuropsychological analysis includes identifying all possible pathways for the identified sets of patterns, ranking the possible pathways based on likelihood for the particular behavioral process and reducing the number of ranked possible pathways based on additional constraints. A system for comparison of obtained signals from an individual to the created knowledge base is provided. These obtained signals are then used to further update the existing knowledge base.

RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.12/302,271 filed on May 26, 2009, which is a National Phase of PCTPatent Application No. PCT/IL2007/000639 having International FilingDate of May 27, 2007, which claims the benefit of priority of U.S.Provisional Patent Application Nos. 60/899,385 filed on Feb. 5, 2007 and60/808,107 filed on May 25, 2006. The contents of the above applicationsare all incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to methods of functional brain imagingand, more particularly, to methods for modeling and/or diagnosingparticular neuropsychological functions via spatiotemporal flow patternsamong functional brain regions.

BACKGROUND OF THE INVENTION

It is known in the field of neuropsychology that behavioral functionsare based upon flow among various functional regions in the brain,involving specific spatiotemporal flow patterns. Likewise, behavioralpathologies are often indicated by a change in the patterns of flow. Thespecific spatiotemporal pattern underlying a certain behavioral functionor pathology is composed of functional brain regions, which are oftenactive for many tens of milliseconds and more. The flow of activityamong those regions is often synchronization-based, even at themillisecond level and sometimes with specific time delays.

Currently, methods for relating behavioral functions to their underlyinglocalized brain activities usually identify discrete participatingregions. Although it is known that multiple regions play a role and thatthe flow from one region to another is important, there are currentlyvery few methods for patterning this flow and relating the patterns toparticular tasks, and those methods which do attempt to pattern the flowdo not seem to yield sufficiently sensitive and specific identificationof the flow patterns underlying specific behavioral functions andpathologies.

SUMMARY OF THE INVENTION

There is provided a method for establishing a knowledge base ofneuropsychological flow patterns. The method includes obtaining signalsfrom multiple research groups for a particular behavioral process,localizing sources of activity participating in the particularbehavioral functions for the research groups, identifying sets ofpatterns of brain activity for the behavioral functions for the researchgroups, neuropsychologically analyzing the localized sources and theidentified patterns for each of the research groups, whereinneuropsychologically analyzing includes identifying a plurality ofpossible pathways for the identified sets of patterns, ranking thepossible pathways based on likelihood for the particular behavioralprocess, and reducing the number of ranked possible pathways based onadditional constraints. After neuropsychologically analyzing thelocalized sources, the method further includes creating a set of flowpatterns from the neuropsychologically analyzed sources and patterns,for each of the research groups and creating a knowledge base of theflow patterns, wherein the knowledge base is then used as a constraintfor the reducing.

There is provided, in accordance with additional embodiments of thepresent invention, a system for neuropsychological brain activityanalysis. The system includes a signal collector for collecting signalsfrom a testing subject, a processor having a pattern generator forgenerating patterns based on the collected signals and aneuropsychological analyzer for translating the generated patterns intoneuropsychologically accurate pathways for particular tasks. The systemfurther includes a flow pattern knowledge base having previouslydetermined neuropsychological pathways and a pattern comparator forcomparing the collected signals to the flow pattern knowledge base, andan output module for presenting results of the comparison.

There is provided, in accordance with additional embodiments of thepresent invention, a knowledge base of flow patterns comprised of atleast one set of flow patterns corresponding to a neuropsychologicalbehavior. The set of flow patterns is established based onidentification and neuropsychological analysis of patterns of brainactivity from multiple subjects during performance of theneuropsychological behavior, and the set may be compared to a flowpattern obtained from an individual subject. Moreover, the knowledgebase may be used to further enhance the identification andneuropsychological analysis.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although methods and materialssimilar or equivalent to those described herein can be used in thepractice or testing of the present invention, suitable methods andmaterials are described below. In case of conflict, the patentspecification, including definitions, will control. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and further advantages of the present invention may be betterunderstood by referring to the following description in conjunction withthe accompanying drawings in which:

FIG. 1 is a flow chart diagram illustration of an overview of a methodof patterning flow in the brain, in accordance with embodiments of thepresent invention;

FIG. 2 is a schematic illustration of a system that can be used for datacollection in accordance with embodiments of the present invention;

FIG. 3 is a block diagram illustration showing the formation of adatabase of data from multiple subjects from different research groups;

FIG. 4 is a block diagram illustration of a neuropsychologicalprocessor, showing its individual components;

FIG. 5 is a flow chart diagram illustration of a possible method ofsource localization, in accordance with one embodiment of the presentinvention;

FIG. 6 is a block diagram illustration showing the components of asource localizer used in the method of FIG. 5;

FIG. 7 is an illustration of a peak map for three electrodes;

FIG. 8 is a schematic illustration of the brain as viewed from abovehaving a first 3-D generated peak map (A) and a second 3-D generatedpeak map (B);

FIG. 9 is a flow chart diagram illustration of a method of patternanalysis, in accordance with a first embodiment of the present inventionwherein source localization is performed prior to pattern analysis;

FIGS. 10A-10B are graphical illustrations of a step of the patternanalysis of FIG. 9;

FIG. 11 is a flow chart diagram illustration of a method of patternanalysis, in accordance with another embodiment of the present inventionwherein pattern analysis is performed prior to source localization;

FIG. 12 is a graphical illustration of a raster plot, which serves as abasis of the pattern analysis of FIG. 11;

FIG. 13 is a schematic illustration of an example of identifyingpatterns comprised of a time-series of region activations includingentailment relations among the regions;

FIG. 14 is a flow chart illustration of a method of neuropsychologicalanalysis, in accordance with embodiments of the present invention;

FIG. 15 is an illustration of a matrix representing regions of thebrain;

FIG. 16 is a schematic representation of different flow patterns foridentified regions, wherein each of the flow patterns is expected tohave different neuropsychological meaning;

FIGS. 17A-17E are schematic illustrations of flow patterns showingconnectivity between functional regions;

FIG. 18 is a schematic illustration of a relation structure;

FIG. 19 is a flow-chart illustration of a method of pattern analysis;

FIGS. 20A-20C are schematic representations of the method of FIG. 19;

FIG. 21 is a block diagram illustration of a system forneuropsychological analysis of an individual, in accordance withembodiments of the present invention;

FIG. 22 is a block diagram illustration of the signal collector of FIG.21;

FIG. 23 is a block diagram illustration of the processor of FIG. 21; and

FIG. 24 is a schematic illustration of an example of a workstation forneuropsychological analysis, in accordance with embodiments of thepresent invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the drawings have not necessarily been drawnaccurately or to scale. For example, the dimensions of some of theelements may be exaggerated relative to other elements for clarity orseveral physical components may be included in one functional block orelement. Further, where considered appropriate, reference numerals maybe repeated among the drawings to indicate corresponding or analogouselements. Moreover, some of the blocks depicted in the drawings may becombined into a single function.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the presentinvention. It will be understood by those of ordinary skill in the artthat the present invention may be practiced without these specificdetails. In other instances, well-known methods, procedures, componentsand structures may not have been described in detail so as not toobscure the present invention.

The present invention is directed to methods for spatiotemporalpatterning of neuropsychological processes. The principles and operationof methods according to the present invention may be better understoodwith reference to the drawings and accompanying descriptions.

Before explaining at least one embodiment of the present invention indetail, it is to be understood that the invention is not limited in itsapplication to the details of construction and the arrangement of thecomponents set forth in the following description or illustrated in thedrawings. The invention is capable of other embodiments or of beingpracticed or carried out in various ways. Also, it is to be understoodthat the phraseology and terminology employed herein are for the purposeof description and should not be regarded as limiting.

The present invention is directed to a tool which can be used forindividual subjects, to analyze their brain activity so as to identifyneuropsychological patterns related to behavior, to correlate theseidentified patterns with particular pathological or non-pathologicalstates, and to aid in therapeutic methods for treating pathologiesassociated with the identified patterns. Methods for creating such atool are described first. Methods for using the tool with individualsubjects are then described in a later section.

Reference is now made to FIG. 1, which is a flow chart diagramillustration of an overview of a method of patterning flow in the brain,in accordance with embodiments of the present invention. The end resultof this method is the creation of a knowledge base, which can then beused as reference for later individual trials on subjects. In order toidentify flow patterns and create the knowledge base, first data iscollected (step 90) from multiple subjects. Data collection is done fora particular behavioral function or pathology by collecting data fromtarget groups as well as from control groups. The data collection couldbe based upon a set of computerized tasks each of the subjects performs,wherein the tasks may include relevant types of stimuli and responses,or data collection may be done during “spontaneous” activity with nosuch specific task. It should be noted that once the initial knowledgebase entry is formed for a specific behavioral function or pathology,data may be continually added to improve the accuracy of the knowledgebase. After data is collected for a specific behavioral function orpathology from the relevant target and control groups, one of twoanalysis lines may be taken. In one embodiment, the sources in the brainfor each activity are first localized (step 100) from the sampledactivity of each subject, as shown with dotted arrows. Sourcelocalization involves identifying, from the sampled activity, regions ofthe brain which underlie it at specific times. Source localization maybe performed in various ways, including known methods and novel ones. Insome cases, the source localization method will yield various possiblesolutions, which may then be sorted according to theirneurophysiological and neuropsychological likelihood. The next step inthis embodiment is pattern analysis (step 200), wherein the localizedsources are arranged as a time-series for each subject. The elementaryevents for the time-series could be filtered waveforms, wavelets,markers of wave amplitudes, etc. It should be noted that in thisembodiment the focus upon regions which repetitively participate inpatterns over many subjects in research groups enables correction ofinaccurate source localizations. For example, if an activity is“smeared” in one subject from region A to a neighboring region B, butconsistently occurs in region A on many subjects of the research group,only region A will occur in a pattern.

In another embodiment, pattern analysis (step 200) is done prior tosource localization (step 100), on the data collected at the initialstep. Then, source localization (step 100) is done for activities whichparticipate in patterns of interest in order to provide a context forthe next step, which is neuropsychological analysis (step 300). Oncepatterns are related to a behavioral function or pathology or to commonbehavioral sub-functions, which are shared by various higher levelbehavioral functions (such as, for example, working memory, attention,etc.), the patterns are analyzed (step 300) in neuropsychological terms.As will be presented, this analysis is used to correct possibleinaccuracies in the source localizations and/or the pattern generation.The pattern analysis further leads to creation (step 400) of a knowledgebase of entries of known patterns relating to behavioral functions andpathologies or common sub-functions. The knowledge base is also basedupon analysis (step 95) of published neuropsychological literature. Theanalysis of published neuropsychological literature is unique in that itincludes a description of possible flow patterns among functional brainregions relating to specific behavioral functions, sub-functions orpathologies. Currently, such functional flow information is notgenerally available in the literature, which usually describes theparticipation of certain regions in a certain behavioral function orpathology, often without reference to their functional flow relationswith other regions in the specific function or pathology or inalternative functions. The knowledge base, in turn, enables improvedsource localization and analysis of spatiotemporal patterns, by posingconstraints regarding possible flow patterns among functional regions.This entire process of improved pattern analysis and localization isautomated by evaluating the likelihood of alternative localizations andpatterns based on this neuropsychological knowledge base.

Reference is now made to FIG. 2, which is a schematic illustration of asystem 10 that can be used for data collection (step 90) in accordancewith embodiments of the present invention. A subject 12 has an array ofelectrodes 13 placed on his head. Each of electrodes 13 is in electricalcommunication with a neuropsychological processor 14, the details ofwhich will be described hereinbelow. The electrical communicationbetween electrodes 13 and neuropsychological processor 14 can be viawires, as shown in FIG. 2, but can also be wireless. Electrodes 13 maybe placed according to known methods. For example, a 10-20 EEG systemmay be used, with activity recording from multiple locations, with areference electrode and a ground. In some embodiments, eye movements(EOG) and muscle movements are recorded as well. Subject 12 is presentedwith a stimulus or a set of stimuli, and activity is recorded during aresponse to the stimulus or stimuli. In alternative embodiments, subject12 is not presented with particular stimuli and responses, and activityis recorded during “spontaneous activity” or during particularactivities. Many such protocols of stimuli, stimuli-responses,action-related and “spontaneous” activity are known in the art, and mayinclude any stimulus-response neuropsychological tests such as Stroop,Wis., etc; tests may include stimulus-only based tests such as mismatchnegativity, BERA, etc; they may include response-only based tests, suchas saccade analysis, MRP, etc; and they may include “spontaneous”activity. Activity is sampled for multiple subjects of the target andcontrol groups. In some embodiments, multiple repetitions are averagedand in other embodiments, only single trials are used. In someembodiments, continuous input may be used. The sampled activity is thensent to neuropsychological processor 14, where the data are amplified,digitized, recorded and used in an algorithm to determine flow patternsand interpret their neuropsychological meaning.

For the purposes of the present invention, any known method for samplingthe brain may be used, including MEG, fMRI, PET, optical imaging or anyother noninvasive or invasive method and/or combinations thereof.However, the use of EEG or event related potential (ERP) for sampling asit relates to flow patterning has the advantage of high temporalresolution (in the millisecond range) (as does MEG, but which issignificantly more expensive). While the tradeoff is in spatialresolution, from a neurophysiological perspective, and while looking fortemporal patterns, the temporal resolution is more critical. Spatialresolution of several cm² may be very informative in neuropsychologicalterms. Furthermore, neighboring regions in the brain generally tend toact in a more synchronous manner and therefore compromise in spatialresolution is often bearable.

Reference is now made to FIG. 3, which is a block diagram illustrationshowing the formation of a database of data from multiple subjects fromthe different research groups. A research group is defined as a group ofsubjects with similar behaviors. The behaviors may be actions oractivities which are performed in a specific way due to a pathologicalcondition, or the behaviors may be non-pathological actions which thesubjects are requested to perform, for example. A research group mayalso include a control group for comparison with a group having orsuspected of having a certain pathological condition or a control groupfor comparison with a group performing the action. Activity data ofsubjects are grouped according to research groups (for example, a targetgroup and a control group, as depicted in FIG. 3). Data 50, 52 from eachof the research groups are sent to neuropsychological processor 14.Although only two research groups are depicted in FIG. 3, it should bereadily apparent that multiple research groups may be included. Datafrom multiple subjects are needed for each research group for generationof patterns by neuropsychological processor 14, as indicated by multiplearrows 54. Neuropsychological processor 14 identifies patterns that arerepetitive over different subjects in the different research groupsbased on the entered data. Thus, the output from neuropsychologicalprocessor 14 is a set of characteristic research group patterns, such ascharacteristic target patterns 56 and characteristic control patterns 58as depicted in FIG. 3. These characteristic patterns are sent toknowledge base 16 and may be used for later comparison with data fromindividual subjects. It should be apparent that this is a dynamicsystem, and that as more patterns are entered, either during research orduring testing of individual subjects, the more robust the resultingcharacteristic patterns will be. Furthermore, knowledge base 16 can thenbe used to help determine flow patterns in individual subjects, bysending the known information regarding a particular activity to a flowpattern comparator, as will be described in greater detail hereinbelow.

Reference is now made to FIG. 4, which is a block diagram illustrationof neuropsychological processor 14 showing its individual components.Neuropsychological processor 14 includes a data collector 18, a sourcelocalizer 20, a pattern generator 22, and a neuropsychological analyzer24. Data collector 18 includes a receiver for receiving sampled activityfrom electrodes 13. In one embodiment, shown by the broken arrows, theactivity is first processed by source localizer 20 in such a way thatindividual locations can be identified. These identified locations arethen sent to pattern generator 22, which identifies a pattern of flowamong the localized sources for the various research groups based on therecordings from electrodes 13. These patterns are then sent toneuropsychological analyzer 24, which analyzes them inneuropsychological terms by matching with the knowledge base. Thismatching analysis will be described in further detail herein below. Inan alternative embodiment, shown by unbroken arrows, the sampledactivity is first sent to pattern generator 22, which identifiesspatiotemporal patterns among the various electrodes for the variousresearch groups. These identified patterns are then sent to sourcelocalizer 20 so as to identify active locations within the brain foreach activity. The identified patterns, which are identified in terms oftheir localized brain regions, are then sent to neuropsychologicalanalyzer 24. In both embodiments, the analyzed neuropsychological flowpatterns can then be sent to knowledge base 16, to further build up,update and correct the library of patterns for each behavioral functionand sub-function. The identified patterns of the neuropsychologicalanalysis are further used to improve the results of the previous sourcelocalization and pattern generation by selecting among possibilities andby offering likely corrections.

The individual components of neuropsychological processor 14 and methodsof use thereof are now described. As a first step, data collector 18collects activity from electrodes 13. For example, many differentwaveforms of varying frequencies and amplitudes over time will becollected for each electrode. All waveforms at all frequencies couldthen be analyzed at each electrode. Although this method of inclusion ofall waveforms at the various frequencies is suggested, it should bereadily apparent that other specific waveform definitions with theircorresponding analysis methods may be used as well, such as, forexample, space filters, blind source separation, or wavelets. Methodsfor frequency separation are known to those skilled in the art, and maybe based on, for example, Fourier transform or different wavelettransforms. The separated bands can then be analyzed for identificationof peak areas of activity in the brain, or analyzed in any other mannerto form a discrete time-series of events at the various electrodes.Combinations of synchronous activities at different frequencies may alsobe used, and may help in description of the waveform and the neuralpattern. It should be noted that other methods of activity analysis,which are not waveform based are also possible.

Reference is now made to FIG. 5 and FIG. 6, taken together, where FIG. 5is a flow chart diagram illustration of a possible method of sourcelocalization 100, in accordance with one embodiment of the presentinvention, and FIG. 6 is a block diagram illustration showing thecomponents of source localizer 20, as used in the method of FIG. 5.Waveforms are collected (step 102) from either data collector 18 or frompattern generator 22, as described above. A frequency separator 30 canseparate (step 104) frequency bands so as to make characteristics suchas peaks of each waveform more readily identifiable. Alternatively, anyother method, such as wavelet analysis, etc. could be used to separatesuperpositioned activity. Also any other wave characteristic could beused instead of peaks, such as wave envelope shape, etc. Signalcharacteristic identifier 32 then identifies (step 106) peaks, wavelets,or other discrete identifiable characteristics from the separatedwaveforms, and from the identified elements generates (step 108) a mapfor all of the electrodes 13, similar to the peak map shown in FIG. 7for three electrodes. As shown in FIG. 7, each of electrodes E₁ . . . E₃has its own peaks each of which may be at a different strength. Thesepeaks are identified and displayed in an array, such that it is possibleto compare peaks on different electrodes for different points in time.It should further be noted that patterns may be identified from combinedactivities at different peaks. Furthermore, the combinations ofsynchronous activities at different frequencies may enable more precisedescription of the waveform, and may more closely relate to the actualneural pattern. Signal characteristic identifier 32, which in thepresent example is a peak map generator, uses the peak map to generate(step 110) a 3-D map of peaks based on the positioning of electrodes 13.

A space localizer 34 then uses the 3-D map of peaks to localize (step112) the sources on the brain. It can offer alternative localizations tothe pattern identified in the map of the scalp electrodes.Alternatively, source localization can be done by known methods such aslow resolution electromotography (LORETA), for example. The localizedactivity is separated into discrete functional regions either“bottom-up” by patterning among subjects in the same experimental group,“top-down” on the basis of neuropsychological knowledge (i.e. Brodmann'sdivision), or with a combination of both. It should be noted that, aswas mentioned previously, localization may be improved via additionalinformation about patterns from knowledge base 16.

Finally, an adjustor 36 may correct (step 114) for any offsets and forspecific pathologies that might result in skewed or missing elementsfrom the pattern. For example, FIG. 8 is a schematic illustration of thebrain as viewed from above having a first 3-D generated peak map (A) anda second 3-D generated peak map (B). For the purposes of description, itis to be assumed that both peak map A and peak map B were generated forthe same activity in two different individuals. In peak map A, peaks arerelatively evenly distributed, while in peak map B, there is a higherdensity on the lateral side. If both of these maps are compared to knownmaps in a database formed from many such trials, they can be corrected.This type of scenario may result from improperly placed electrodes, orfrom variations in head anatomy among subjects. Additionally, there maybe some scenarios where a particular pathology destroys a portion of thebrain. If there are missing peaks in a particular region that can beattributed to such a pathology (based on the database and possiblystructural imaging input), corrections can be made for these situationsas well.

Reference is now made to FIGS. 9 and 10, taken together, where FIG. 9 isa flow chart diagram illustration of a method of pattern analysis 200,in accordance with a first embodiment of the present invention whereinsource localization is performed prior to pattern analysis, and FIG. 10is a graphical illustration of a step of pattern analysis 200, as willbe described hereinbelow. First, locations found by source localizer 20are mapped (step 202) onto 3-D grids over time. As shown in FIG. 10, ateach time period, a different three-dimensional map of the variouslocations is generated, showing (step 204) signal strengths at locationsfor a primary time period A and a secondary time period B. Additionaltime periods may be included as well. Generally, the signal strengthsand their spatial distributions change after a period of time (usuallyin the milliseconds—up to tens of milliseconds range). Thus, forexample, at one time, particular localizations may demonstrateparticular signal strengths (shown for example in FIG. 10 as strengthsof 10 and 5 at two locations respectively), while at another time, otherlocalizations may demonstrate different signal strengths (shown forexample in FIG. 10 as strengths of 12 and 3 respectively). These stepsare repeated for all of the subjects within the particular researchgroup. Once patterns from multiple subjects are collected, patterngenerator 22 searches for (step 206) repetitive patterns among subjectsof the same research group. The patterns involve the timed activation ofsets of regions, with temporal, spatial and strength tolerance. This isbased upon counting the number of times a particular signal strength atspecific spatial location (all, as stated, with tolerance) is obtainedat a particular time period, pairs of such events, and so on to largerand larger groups of such events. Thus, a simple counting method is usedto determine a pattern wherein patterns of activation of a set ofregions, each with its strength/temporal/spatial characteristics thatare repetitive among subjects of a certain research group, areidentified—all within their dynamic tolerances. It should be readilyapparent that the greater the number of inputs (i.e., the number ofexperimental subjects used), the more robust the pattern analysis willbe.

Reference is now made to FIG. 11 and FIG. 12 taken together, where FIG.11 is a flow chart diagram illustration of a method of pattern analysis200′, in accordance with another embodiment of the present inventionwherein pattern analysis is performed prior to source localization, andis performed on waveforms directly obtained from electrodes 13, (or anyother chosen characteristic of the sampled activity), and FIG. 12 is agraphical illustration of a raster plot, which serves as the basis ofpattern analysis 200′, as will be described hereinbelow.

First, pattern generator 22 sets (step 203) conditions (such asthresholds) for waveforms obtained from electrodes 13. In oneembodiment, a binary type of threshold is used, wherein peak valuesabove the threshold are included and values below the threshold areexcluded. In another embodiment, a gradual scale may be included. Asstated, not only peaks, but also wavelets, or other discreteidentifiable elements for each electrode for the particular subjectcould be utilized. In one embodiment, waveforms which are of varyingfrequencies are separated out, and peaks are identified (step 205) foreach frequency at each electrode for each subject. This step is repeatedfor all electrodes per subject. Next, pattern generator 22 forms (step207) a raster plot for the full set of electrodes showing peaks overtime. An example of a raster plot is depicted in FIG. 12. It should benoted that tolerances for time may be included as well, such that if thepeak occurred within the determined tolerance it will be counted. Itshould further be noted that patterns may be identified from combinedactivities at different peaks. Furthermore, the combinations ofsynchronous activities at different frequencies may enable more precisedescription of the waveform, and may more closely relate to the actualneural pattern. These steps are repeated over multiple subjects and theresults of the peak identification of multiple subjects over variousfrequencies over time are input into a processor which is configured toidentify (step 209) a pattern of peaks over time for multiple subjectsfor a particular research group. Specifically, pattern generator 22searches for repetitive patterns among subjects of the same researchgroup. The patterns involve the timed activation of sets of electrodes,with temporal, spatial and strength tolerance. This is based uponcounting the number of times a particular signal strength is obtained ata particular time period, pairs of such events, and so on to larger andlarger groups of such events. Thus, a simple counting method is used todetermine a pattern wherein patterns of activation of a set ofelectrodes, each with its strength/temporal/spatial characteristics thatare repetitive among subjects of a certain research group, areidentified—all within their dynamic tolerances. It should be readilyapparent that the greater the number of inputs (i.e., the number ofexperimental subjects used), the more robust the pattern analysis willbe. Those patterns are later used for comparison, as will be describedfurther hereinbelow. The identified patterns are then sent to sourcelocalizer 20 for source localization.

Reference is now made to FIG. 13, which is a schematic illustration ofan example of identifying patterns comprised of a time-series of regionactivations, but which also suggest entailment relations among thoseregions. This is based on the counting methods, in accordance withembodiments of the present invention. Although the following descriptionrefers to identified regions, as in the first embodiment of the presentinvention wherein source localization is done prior to pattern analysis,it should be readily apparent that similar methods may be used foridentifying patterns based on electrode waveforms (or any other activitycharacteristic), as in the second described embodiment of patternanalysis. As shown in FIG. 13, and as described above with respect topattern analysis, regions A, B and C are activated (at a certainstrength, at a certain time) for the particular research group. However,not all regions become activated for all subjects, and timing may vary.Thus, the total number of times that any combination of the regions (forexample, region A and B as shown in line 1 in FIG. 13) were activated atspecific timing and with a specific strength for each research group arenoted. The number of participating regions may be any number from oneand up and each region can participate more than once without limitationat different times. In the current context, the word “entailment” isdefined as a correlative relationship between two events, which may hintat causality. Thus, if event A entails event B, then event A correlatesto event B and also might have a causal relationship with event B. Someinitial conclusions as to the entailment relationships between regionactivations (for example, A entails B which entails C versus A entailsboth B and C independently) may be made. Those initial conclusions arebased upon the relative timing among the participating regions. Forexample, in a first scenario, if one knows the relative timing of Cafter A over the different subjects in a research group (for example Coccurs between t1 and t2 milliseconds after A) and then one looks atactivations of C only after A together with B, if the relative timingperiod does not change significantly by including B, then B does nottend to contribute significant new information with regard to the timingof activation of C after A. On the other hand, in a second scenario, ifthe relative timing of C seems significantly related to B, then it doescontribute new information. The first scenario is most likely indicativeof an independent entailment of both B and C by A, while the secondscenario is most likely indicative of a dependent one. It should benoted that by taking into account information from knowledge base 16, itis possible to improve the sensitivity and specificity of the patterns.In this way, a set of patterns is generated based on multiple subjectsfor each research group, and this pattern is then updated based on anynew inputs or trials that are added later. Another way to achieve asimilar result is via comparisons of spatial or strength relations amongthe regions (instead of temporal relations as presented here). This toowill show whether there is additional dependent information or not.

Reference is now made to FIG. 14, which is a flow chart illustration ofa method of neuropsychological analysis (step 300), in accordance withembodiments of the present invention. The purpose of theneuropsychological analysis step (step 300) is enhancement of thepatterns generated up until this point, as well as their interpretationinto neuropsychological terms. Knowledge base 16 is used to helpevaluate specific patterns identified by pattern generator 22, and basedon the analysis to determine flow patterns including a sequence andduration of activated locations in the brain for each behavioralfunction and sub-function. These flow patterns are created for both thenormal and pathological states, and knowledge base 16 including theseflow patterns are accessible for comparison purposes for evaluatingsingle subjects.

Neuropsychological analysis bridges bottom-up and top-down findings. Thebottom-up input is a time-series of activities of functional regionswhich had been previously identified (in the pattern analysis phase) asbeing repetitive in at least one research group. The top-down input isthe knowledge base including functional relations among brain regions.The output of the analysis is a description of possibleneuropsychological flow patterns and translation of these flow patternsinto neuropsychological terms. Automatic suggestions for correction whenthe comparisons are imperfect may be included in the output.

In the top-down input, several levels of relationship indicators may beused to relate certain regions in the brain to others and thus to form aflow pattern. The first level may include a matrix or otherrepresentation of functional brain regions showing relationships betweenany two regions in the brain. The matrix is created (step 302) on thebasis of new experimental data, produced in the manner described aboveor on the basis of data available in the literature, which providesscientific information regarding relationships of certain regions toother regions in particular behavioral functions. The data is rarelydirectly available in the literature in such a format and often must bededuced from the reports of activation of various specific regions inthe specific behavioral function and in other functions and fromknowledge regarding anatomical and functional relations among regions.Neuropsychological analyzer 24 retains an updateable database of theserelationships, for example in matrix form. An example of a matrix isshown in FIG. 15, wherein regions R₁ . . . R_(N) of the brain arerepresented, down and across. Thus, each box represents a generalfunctional region. Within each box, more specific subcategories of theregion are distinguished from one another. As an example, if Region 2represents vision as it relates to recognition of the human face, theparticular subcategories might include, for example, familiar vs.unfamiliar faces. For a given behavioral function, each region may evokeactivity in other regions. With rigorous analysis, information aboutfunctional relations among regions can be deduced to a degree from theneuropsychological and neurophysiological literature, and is furthercompiled by experimental methods such as the ones described in thepresent application. Thus, pre-existing knowledge about which regionsare represented in what order for a particular state can help build theknowledge base, but actual data taken from the present systemsignificantly aids in building the knowledge base, and the knowledgebase is adjusted accordingly. It should be readily apparent that amatrix is only one way of depicting flow patterns, but otherrepresentations are possible as well. As shown in FIG. 15 and statedabove, one subcategory may be generally known to lead to a particularregion, while another subcategory from the same source region is knownto lead to a different region. This information can help indetermination of a flow pattern for specific behavioral functions andparticularly for sub-functions. Flow patterns can be determined forcommon sub-functions such as, for example, inhibition, working memory,attention, etc. Alternatively, flow patterns can be determined forparticular higher order behavioral functions. The preference among thepatterns in the knowledge-base, when comparing them with the patternsfrom the analysis of previous stages as described above is: (1) patternsfor the precise behavioral function at hand, (2) patterns tosub-functions, which are expected in the behavioral function at hand,and (3) patterns which are based upon the functional relations betweenregions (as in the matrix format).

Returning now to the flow-chart illustration of FIG. 14, the above threecomponents, in their preferred order, are used to determine (step 304)all possible flow patterns for a given time-series of region activities.For example, as shown in reference to FIG. 16, there may be manydifferent flow patterns involving the identified regions at theirspecific times of activation, and each pattern is expected to havedifferent neuropsychological meaning. As shown in FIG. 16 as an example,if the regions identified are regions A, B, C and D, one possiblepathway would be A leads to B which leads to C which leads to D, asshown with solid arrows. Another possibility might be that A leads to B,C and D all together, as shown with short dotted arrows. Yet anotherpossibility might be that A leads to B, and B leads to C and D together,as shown with long dotted arrows. Each of these possibilities mightunderlie quite a different neuropsychological process. For example, anauditory sensation might activate association in a higher representationarea (for example of a voice of a friend), which then in turn mightactivate association of emotional significance. Alternatively, anactivation of another auditory sensation, such as the roar of a tigermight activate by itself the emotional significance representation atits relevant regions and also activate independently the higherrepresentation area. Thus the arrangement of flow among the same regionswill have quite a different neuropsychological meaning. Once allpossible pathways have been determined, the likelihood of it being onepathway over another is calculated (step 306) for the particularbehavioral function. This likelihood is based as stated on the threecomponents of the knowledge-base as described above, and is then used tohelp create (step 308) flow patterns and to build the knowledge base 16.Automatic suggestions for correction when the comparison to known flowpatterns is imperfect may be included in the output.

The flow of the algorithm for comparing obtained patterns to thepatterns in the knowledge base and for translating the obtained patternsinto neuropsychological terms can be as follows. First, on the basis ofthe pair level comparison (matrix as described above), the time-seriesis scanned, and all possible relations among regions are marked. Thematrix may also include temporal constraints (for example, region A canactivate region B at a certain temporal delay with tolerance). Thosedelays are then imposed in the scan. The output of this stage is eithera graph, which is composed of all the possible relations among regions,or a set of isolated sub-graphs, each composed of all the possiblerelations among its regions. The sub-graphs are separated from oneanother, because there is no legitimate relation between at least oneregion in one sub-graph and one region in the other sub-graph. For eachsub-graph (or if there is one graph, for the entire graph) all possiblecombinations of relations (depicted, for example, as arches) which wouldstill span the graph are computed. For example, if a sub-graph iscomposed of regions A, B and C and it is known from the matrix that atthe relevant temporal delays, A can activate B, A can activate C and Bcan activate C, the possible combinations for the sub-graph would be:(1) A activates B, which activates C; (2) A activates both B and C; and(3) A activates both B and C and the activation of B further activatesC. All possible combinations are thus described and counted.

A general grade of the match between the bottom-up and the top-downfindings is given based on the number of sub-graphs. The lesscomprehensive the graph (the more sub-graphs there are) the lower thegrade.

An automatic search is evoked to suggest improvements to the results, sothat the graph is more comprehensive. This means that the relationsbetween each 2 sub-graphs are scanned to find possible manners tocombine them at a minimal cost, as will be hereby described.

The minimal cost corrections could be either via suggestions ofcorrection to the bottom-up process, to the top-down process or both.They are based on the ability to replace a certain region in one (ormore) of the sub-graphs, to remove it, or to add a new region. Thisability is based on specific considerations, as follows. In correctionof the source localization component and with regard to the nature ofsource localization algorithm employed, the improvement is in findingalternative regions which may have been active and which would connectthe sub-graphs. For example, often neighboring regions, which areincluded, are likely to be erroneously excluded. The analysis is basedin this case on the anatomical distance between regions. That is, forexample, if a region could be added/replaced which is directly aneighbor of an existing region, it may have a cost of 1; if there is anadditional region between them, it may have a cost of 2, etc. Thus, ascan is performed for minimal cost of anatomical distances ofadditions/replacements/deletions which combines the sub-graphs inaccordance to known features of the localization algorithm.

In correction of the pattern analysis component and with regard to thenature of the pattern recognition algorithm employed, a similar scanwould look for regions that may have been just out of the toleranceranges (or alternatively for deletion just within tolerance ranges) orjust below (or alternatively for deletion just above) threshold andwhich enable connecting the sub-graphs. Here the cost is based ondeviation from thresholds and tolerance margins.

In correction of the knowledge-base, it is known that if region A tendsto activate region B, which tends to activate region C, then to a lesserdegree region A will often directly activate region C. A correction isthus based on adding such “jumps” and the cost is the “jump” distance.Once the knowledge base grows and as it is directly linked to publishedreferences, another correction is to point out published referenceswhich have shown relations currently excluded from the knowledgebase, oralternatively to point out published references which state that acurrently included relation is incorrect. The number of relevantreferences and their scientific significance (impact factor, etc.) areevaluated as the basis for cost in this case.

The sub-graph combinations may also be ranked. The ranking is based onthe hereby described preference. Sub-graph combinations which involvepaths that are task-related for the relevant task employed are highlyranked. Paths which relate to general sub-functions also gain rankscores (it is possible that more than one sub-function will be found andthe rank gains can be combined accordingly). The basic rank is forpair-level relations. Any of those three levels also have inter-levelpreference rank. Thus, all in all, each sub-graph combination is rankedaccording to likelihood as well.

Finally, according to the translation component of the knowledgebase,each graph, either corrected or basic, is translated intoneuropsychological terms. The translation is also based upon the threecomponents—namely task-specific flow patterns, behavioral sub-functionflow patterns and pair level.

Several examples of flow patterns showing connectivity betweenfunctional regions is shown in FIGS. 17A-17E and associated Table 1which relates functional regions to the numbering on the figures. Thesediagrams were formed based on published literature. It should be readilyapparent that these are merely examples, and do not necessarilyrepresent actual patterns. Moreover, many alternatives may be suggestedbased on theory and experimental findings. FIG. 17A is a diagrammaticrepresentation of global interrelationships between an action,perception, executive function and attention. FIGS. 17B-17E are morespecific diagrammatic representations of perception, executive function,action and attention, showing relationships and interrelationshipsbetween different areas of the brain which are functional during theseactivities. Similar models may be created for particular tasks,behaviors or activities, as described with respect to the presentinvention.

TABLE 1 Modules Functional module Hemi BA Neuroanatomy 1. Perception1.1. Visual 1.1.1. Primary visual X 17 1.1.2. Secondary visual X 181.1.3. Tertiary visual 1.1.3.1. Objective oriented Lt 19 1.1.3.2.Subjective oriented Rt ″ 1.2. Auditory 1.2.1. Primary auditory Bi 411.2.2. Secondary auditory Bi 42 1.2.3. Tertiary auditory 1.2.3.1.Objective oriented Lt 21, 22 1.2.3.2. Subjective oriented Rt ″ 1.3.Somatosensory 1.3.1. Primary somatosensory X 1, 2, 3 1.3.2 Secondarysomatosensory X Parietal operculum 1.4. Pain 1.4.1. Primary pain XPosterior Insula 1.4.2. Secondary pain 1.4.2.1. Objective oriented LtAnterior Insula 1.4.2.2. Subjective oriented Rt ″ 1.5. Heteromodalcontent (a) Objective oriented Lt (b) Subjective oriented Rt 1.5.1.Visual-Auditory 37, 20 1.5.2. Visual-Somatic 39 1.5.3. Global 38 1.6.Heteromodal spatial 1.6.1. Body X + Rt Superior parietal lobule 1.6.2.Milieu X + Rt Inferior parietal lobule 1.7. Short term content direction1.7.1. Objective oriented Lt Ventral posterior cingulum 1.7.2.Subjective oriented Rt Ventral posterior cingulum 1.8. Short termspatial direction X Dorsal posterior cingulum 1.9. Association 1.9.1.Objective oriented Lt Hippocamus + parahippocampal 1.9.2. Subjectiveoriented Rt Hippocamus + parahippocampal 2. Executive function 2.1.Significance evaluation 2.1.1. Objective oriented Lt Amygdala 2.1.2.Subjective oriented Rt ″ 2.2. Executive direction (a) Content directionLT (b) Spatial direction RT 2.2.1. Top level 9, 10 2.2.2. Basic level46, 47 2.3. Outcome prediction 2.1.1. Objective oriented Lt Ventromesialprefrontal cortex 2.1.2. Subjective oriented Rt Ventromesial prefrontalcortex 3. Action 3.1. Abstract action 3.1.1. Content action Lt 44, 453.1.2. Spatial action Rt ″ 3.2. Implementation X Medial cingulum 3.3.Complex action 3.3.1. Body X 6 3.3.2. Eyes X 8 3.4. Basic action X 43.5. Action maintenance II Cerebellum 4. Attention 4.1. Processselection 4.1.1. Executive selection 4.1.1.1. Content selection LtVentral basal ganglia 4.1.1.2. Spatial selection Rt Ventral basalganglia 4.1.2. Implementation selection X Dorsal basal ganglia 4.2.Perceptual attention U Locus Ceruleus 4.3. Executive attention U Ventraltegmental area 4.4. Action attention U Raphe nuclei

A method of pattern recognition in accordance with additionalembodiments of the present invention is now described.

Definitions:

-   Entity—either (1) a basic symbol in the input order series, which    is, in the current application, an active functional area, or (2) a    pair as it is defined below. The first entities are basic symbols    and as the algorithm runs, new entities are formed, which can be    composed of 2 basic symbols, and then of a basic symbol and a    previous pair, 2 previous pairs and so on.-   Occurrence—a specific event of an entity in the order series. Each    entity, whether basic or complex, can occur in many entries. As will    be presented below, an occurrence of a complex entity can spread    over more than one entry. As a means of convection, complex entities    will be considered as occurring in the first entry they involve. As    will be presented below, they will include the necessary information    regarding the other entries involved in them.-   Pair—a relation between 2 entities. The pair could be built of    entities in the same order entry, or of entities from different    order entries—for example with a delta of 1 entry, 2 entries etc.    Thus, the definition of each pair involves also the delta between    the order entries. If the 2 entities are of the same entry, the    delta is 0. Thus the pair (i,j|0) means a relation between entity i    and between entity j in the same entry; the pair (i,j|1) means a    relation between entity i and entity j in a consecutive entry; the    pair (j,i|1) means a relation between entity j and entity i in a    consecutive entry. Note that pair (j,i|0) is the same as pair    (i,j|0) because at the same entry, there is no order difference.-   Ancestor entities—defined for complex entities, composed of at least    one pair, these are the entities which are paired with other    ancestor entities at any step in the process of creating the complex    entity. For example, suppose entities i & j were paired as (i,j|0)    and then paired with entity k as ((i,j|0),k|0). Suppose also that    entities l & m were paired as (l,m|0). Now suppose that both complex    entities were paired as (((i,j|0),k|0),(l,m|0)|0). Let us term this    new entity—x. The ancestor entities of x are then: 1.((i,j|0),k|0),    2.(l,m|0), 3.(i,j|0), 4.k; 5.l, 6.m, 7.i & 8.j.-   Independent pair—a pair that reflects a relation which does not    result from other pairs.-   Dependent pair—a pair that reflects a relation which results from    other pairs. For example, if i, j and k are entities, which hold the    following relations: i→j→k, then (i,j|Δ_(ij)) and (j,k|Δ_(jk)) are    independent pairs, while (i,k|Δ_(ij)+Δ_(jk)) is a dependent pair.    Statistical significance of a pair (i,j|Δ_(ij))—If entity i occurs    x_(i) times in an order series, which includes n entries altogether,    and entity j occurs x_(j) times in the same order series, then the    probability of occurrence of entity i is x_(i)/n and the probability    of occurrence of entity j is x_(j)/n. The probability of random    co-occurrence of entity i & j, with any Δ_(ij), in the same order    series entries, p_(ij) is the product of (x_(i)/n) & (x_(j)/n). This    is provided that Δ_(ij) is small enough when compared to n and    neglecting near edge distortions, by which occurrences of i in the    last Δ_(ij) entries of the order series could not be followed by j.    If the real co-occurrence of entities i & j, with a specific Δ_(ij),    in the same order series entry is x_(ijΔij), it is possible to use    the binomial distribution to evaluate the statistical likelihood of    it. The formula of the binomial distribution is

${F\left( {x,p,n} \right)} = {\sum\limits_{i = 0}^{x}{\begin{pmatrix}n \\i\end{pmatrix}(p)^{i}\left( {1 - p} \right)^{({n - i})}}}$in our case: x is x_(ijΔij), p is p_(ij) and n is n. The value computeddenotes the likelihood that x_(ij) co-occurrences will occur randomly.The smaller this value the greater the likelihood that the event is notrandom. We use an arbitrary threshold of 0.001 to define significantpairs. Note that both independent and dependent entities can besignificant.

The goal of this method of pattern analysis is to start from the orderseries data and to expose the activity relations structure, or therelation patterns. As an example, suppose the symbols in a given datasetare i,j & k. The activity relation patterns, or the activity relationsstructure, in the dataset are precisely the following: symbol i entailssymbol k in the same order entry, with a likelihood of 0.5, and symbol itogether with symbol j in the same order entry entail symbol k in thefollowing order entry, with a likelihood of 0.75. Furthermore, supposethat symbol i and symbol j occur spontaneously with a random likelihoodof 0.4 and 0.8 correspondingly and symbol k does not occurspontaneously. This relations structure might lead to the followingorder series:

Entry Symbol 1 2 3 4 5 6 7 8 i: ACTIVE ACTIVE ACTIVE ACTIVE j: ACTIVEACTIVE ACTIVE ACTIVE ACTIVE ACTIVE k: ACTIVE ACTIVE ACTIVE

While the current algorithm presented is aimed for an order series data,the theoretical principles of the algorithm, which will be explainedbelow, would be applicable to a time series, which basically involvestolerance in the precise timing among related occurrences of entities.Furthermore, while the current activity analyzed is in terms ofactive/inactive, the theoretical principles are also applicable to ascale of possible values for the occurrences of an entity, which wouldrequire strength tolerance. Note that, as is evident from the exampleabove, in a finite dataset, there could be various possible datastructures. It is possible to add a possibility to form allalternatively plausible relation structures for a given dataset alongwith rankings of likelihood.

A relation structure is divisible into pairs and simple groups.Reference is now made to FIG. 18, which is a schematic illustration of arelation structure. Each relation has its own strength of significanceand temporal delta. Furthermore, if the temporal delta is more than 0,the relation also has temporal direction. However, for the purpose ofthe current point, it is possible to ignore those characteristics. Arelation structure is composed of pairs of entities and of simple groupsof entities. The pairs are evident in the presented structure—forexample, (j,k); (k,m); (j+k,n+o); etc. A simple group is composed ofentities, which tend to occur together. For example in the presentedstructure, the node uniting i, j & l marks a group. Note that j,k & mare also connected and in a sense occur together, but this results fromthe pairs (j,k) and (k,m) which happen to co-occur. On the other hand,the entities comprising a simple group, or in the example, the entitiesi,j & l, occur together significantly beyond random co-occurrence of thevarious pairs composing it. In terms of dependent probabilities, in thetrio j, k & m, if it is known that k occurred, knowing further that joccurred as well, does not increase the likelihood for m to occur.However, in the trio i, j & l, knowing that any entity occurred togetherwith any other entity increases the likelihood of the 3^(rd) entity tooccur. The dependent probability considerations are extendable to simplegroups larger than trios in a straightforward manner.

More complex structures are also divisible into pairs and simple groups.Already the structure presented involves a pair composed of prior pairs((j,k),(n,m)). More complex relations are also divisible in a similarmanner to pairs and simple groups. Note that multiple relations amongbasic symbols or complex entities are also possible. For example, in theabove structure, entities i & l could have been also paired directly inaddition to the group they form with j. Nevertheless, this additionalrelation is still a pair. Also note that a negative relation betweenentities, complex or basic, is also possible. A negative relation meansthat the involved entities tend to co-occur significantly less than whatwould have been expected randomly. Or in other terms, if one of thoseentities or group of entities occurs, the likelihood of the otherentailed entity or group of entities to co-occur reduces significantly.Again a negative relation is still a pair or a simple group relation.

Thus, it is possible to divide relation structures to pairs of entitiesand simple groups, down to the level of basic symbols. Furthermore, notethat each simple group could be described in terms of pairs, where atfirst 2 entities are paired to form the core pair of the group, thenthis core pair is paired with another entity to form the core trio andso on. Thus, it is possible to divide any relations structureconsistently to 2 parts, down to the level of the basic symbols. This,however, means that for a given order series dataset, it is possible toexpose the underlying relations structure on the basis of pairing fromthe basic symbols upwards. It is only necessary to pair correctly thealgorithm presented here. Choice of threshold, sample size and methodfor calculating statistical significance will all determine sensitivityand/or specificity of the method.

In order to maximize correct results, strongest relation pairs areincluded as new entities. Therefore it should be noted that if there are2 independent relations—for example (i,j) and (j,k) which underlie to adependent relation (i,k), the 2 independent relations are alwaysstronger then the dependent one. This is because the dependent relationis a mere random co-occurrence of the independent relations and itsprobability is the product of the probabilities of the independentrelations and therefore it is smaller. Thus, if pairs are selected inthe algorithm's ordered pairing process, it means they are independent.We further reduce from the dependent pair count the occurrences whichresult from the co-occurrence of the 2 independent pairs and thus, ifthe dependent pair does not occur significantly beyond thoseoccurrences, it will be excluded as insignificant. It will be includedonly as dependent by later unification of the 2 independent relations.Note that if it occurs significantly also independently beyond thoseoccurrences, it is selected as independent and regains all the reducedoccurrences. Lastly, as will be described in the below, for each newlyselected pair, its relations with the other entities are computed. Ifthis new pair (i,j) relates strongly with the entity k, it will form asimple group ((i,j),k). However, if k relates more strongly to one ofthe pair's comprising entities, it will relate to it and the 2 pairscould later be united—for example, ((i,j),(j,k)).

Reference is now made to FIG. 19, which is a flow-chart illustration ofa method of pattern analysis, and to FIGS. 20A-20C, which is a schematicrepresentation of the method of FIG. 19. First, a basic list of entitiesis built (step 502). This list of entities is a list of basic symbols.Next, an ordered list of all significant pair relations between variousentities in the basic list is computed (step 504). As defined above,pairs could involve either entities from the same entry or entities fromdifferent entries, with a certain delta between them. The list isordered, so that the most significant pair comes first, followed by thesecond strongest and so on. Note that only significant pairs, below thesignificance threshold, are included in the list. Next, the strongestpair is selected (step 506) from the top of the list, and added to theentities' list as a new entity. It is also removed from the significantpairs list. Next, occurrences of the strongest pair in the relevantentries of the order series are marked (step 508). This marking alsoincludes occurrences which might have been previously designatedinactive. This is because once a pair is selected, it is allowed to pairwith other entities even in occurrences where it is a merely a subset ofpreviously discovered entities. Next, the statistical significance ofthe pairs it forms with other previous entities is calculated (step 510)according to their co-occurrence in the order series. Next, if the newentity is more than a pair of two basic symbols (ie, includes at leastone previous pair), then a first ancestors list and a second ancestorslist are built (step 512). The first ancestors list includes all of theentities which are ancestors to the first entity of the new pair. Thesecond ancestors list includes all of the entities which are ancestorsto the second entity of the new pair. All pairs of entities from thefirst and second ancestors lists are reviewed. For each ancestor pair,if the pair is in the significant pairs list, then the order series isreviewed and for every entry which includes an occurrence of the newentity, the ancestors pair is designated (step 514) as inactive. Notethat it may have already been inactive if it was already an ancestorspair in a previous iteration. Ancestor pair occurrences are recounted asare counts of its two comprising entities, without the inactiveoccurrences. Note that the count of occurrences of the comprisingentities is not generally reduced, but only locally reduced, in relationto the specific inter-ancestors pairs. In their relations with otherentities, which are not on the 2^(nd) ancestors list, the ancestorentities counts and co-occurrence counts stay the same. Ancestors pairsignificance is re-computed, and its order in the significant pairs listis updated accordingly or removed from the list. If ancestor pairs arenot significant, the pair is discarded (step 516).

In some embodiments, troubleshooting and automated evaluation of theknowledge base, source localization or pattern analysis may be done bycomparing analyzed patterns to known patterns already in the knowledgebase. For example, if there is a slight discrepancy in region, whereinan analyzed pattern includes a region or regions which is different thanpreviously determined and stored patterns, if the regions areneighboring it is likely a source localization problem. Alternatively,if the regions are distant from each other, it is likely a knowledgebase problem. Another example involves a correction of the knowledgebase. According to the matrix knowledge base, each of two regions areeither directly related, or are related via a certain number ofintervening regions. If this evaluated distance at the knowledge baselevel is in discrepancy when compared to the identified patterns in arepetitive manner, which cannot be explained by alternative corrections(as the one suggested above to localization and others), then there islikely a knowledge base imperfection which requires correction. Yetanother example is correction of the identified flow pattern analysis byhigher resolution search for a specific region, which is predicted fromthe knowledge base and might have just fallen short of the threshold ortolerance parameters set.

The knowledge base 16 created in the manner described above is used inthe system of the present invention, as will be described hereinbelowwith respect to FIG. 21. Reference is now made to FIG. 21, which is ablock diagram illustration of a system 600 in accordance withembodiments of the present invention. System 600 includes a signalcollector 602 configured to collect signals from a testing subject 603,a processor 604 for processing the signals, and an output module 606 fordisplaying the results of the processed signals. Reference is now madeto FIG. 22, which is a block diagram illustration of signal collector602 shown in greater detail. In some embodiments, signal collector 602includes electrodes 13 to be placed on the head of testing subject 603,and an amplifier 608 for amplification of signals received fromelectrodes 13 in response to an activity or task by testing subject 603.In some embodiments, collector 602 further includes tasks. It should bereadily apparent that other types of signals may be collected and thatsignal collector 602 is not limited to the description herein. Forexample, signal collector 602 may include fMRI, PET, optical imaging,MEG or any system or method (and their combinations) for obtaininginformation related to brain function in a human. In some embodiments,subject 603 is not presented with particular stimuli and responses, andactivity is recorded during “spontaneous activity” or during particularactivities. Many such protocols of stimuli, stimuli-responses,action-related and “spontaneous” activity are known in the art, and mayinclude any stimulus-response neuropsychological tests such as Stroop,Wis., etc; tests may include stimulus-only based tests such as mismatchnegativity, BERA, etc; they may include response-only based tests, suchas saccade analysis, MRP, etc; and they may include “spontaneous”activity.

Reference is now made to FIG. 23, which is a block diagram illustrationof processor 604, showing the components in greater detail. Processor604 includes an input adjustor 610, a pattern comparator 612 and a copyof knowledge base 16 created from experimental and publishedinformation, as described above. Input adjustor 610 is configured toadjust input from signal collector 602 so that it conforms to the flowpattern information found in knowledge base 16. Thus, in one embodiment,input adjustor 610 includes a source localizer 20 and is configured toperform source localization so as to identify regions of the brain beingactivated by the activity or task performed by testing subject 603. Inanother embodiment, input adjustor 610 is configured to identify peaks,wavelets, or other discrete identifiable elements over time forelectrodes 13. In this second embodiment, the knowledge base patternsare also described at this electrode level. Pattern comparator 612 thentakes the adjusted input and compares it to flow patterns includedwithin knowledge base 16. Pattern comparator 612 is configured toidentify a pathology or normal state based on comparison of the adjustedinput and the stored information regarding pathological or normalpatterns. Moreover, pattern comparator 612 may translate the determinedpatterns even if parts of the patterns do not relate to a specificpathology, by parsing the activities according to their likelihoods ofmatches with stored flow patterns, as will be described furtherhereinbelow.

In parsing, over time and as more regions are introduced, thepossibilities of patterns to match up with are sharpened. Thus, forexample, at a single timing with a few regions, many different patternsmay fit the time-series of region activations obtained from the singlesubject. However with more sampled regions over time, certain patternsbecome more likely. It should be noted that even when particular regionsor sequences of electrodes are identified, timing at the particularregions or electrodes is important in distinguishing between flowpatterns. The process of parsing eventually results in a matching up ofthe obtained patterns with saved patterns from the database. Similarlyto the above description regarding the neuropsychological analysis, theparser may work on several levels, wherein at a first level,combinations of pairs of regions are identified. At a second level, theparser identifies general behavior based on flow patterns for particularbehavior sub-functions. At its most specific level, the parser canidentify patterns directly relating to specific behavioral functions,such as an activity or task being performed by testing subject 603. Thealgorithm described with respect to the development of the research toolmay also be used for neuropsychological analysis for the individualsubject.

Output module 606 may be any suitable display, such as a monitor or mayinclude graphs or reports relating to the obtained results. Thisinformation can either be used to detect effects of treatment onfunctional brain activity or to direct treatment, or it may be used forexperimental or educational purposes. The analysis could be performedand presented offline or online during the sampling process. In oneembodiment, output module 606 includes a feedback loop as part of acomplete workstation (described below with reference to FIG. 24),wherein results from processor 604 are automatically used to provideadditional stimuli to testing subject 603. An example of a system usinga feedback loop is presented in FIG. 24, which is a block diagramillustration of system 600 in accordance with one embodiment of thepresent invention.

In the example depicted in FIG. 24, system 600 is a workstation whichmay enable a professional (physical or occupational therapist, speechpathologist, rehabilitation doctor, neurologist, psychiatrist etc.) toobserve and direct brain effects during treatment, either with currentmethods or with novel methods. The workstation incorporates informationregarding identified functional patterns and their change prior andduring practice from the above described technology together with inputregarding the treatment protocols and their peripheral effects outsidethe brain sampled by various technological modalities. This enables anintelligent direction of the treatment either off-line or on-line duringtreatment. Part of the direction is based upon peripheral biofeedback orbrain neurofeedback methods, which are pattern related. Virtual realitytechnology may also be incorporated in the work station.

As shown in FIG. 24, system 600 could include electrodes 13 placed onthe head of testing subject 603. Alternatively, any of the methodsdescribed above such as fMRI, PET, etc. could be used. Electrodes 13 areconnected to amplifier 608, for amplifying signals obtained byelectrodes 13, and sending the amplified signals to processor 604.Processor 604 includes pattern comparator 612 and knowledge base 16, asdescribed above. Processor 604 provides output to output module 606,which in the present embodiment includes a feedback loop provider 630.Output module 606 with feedback loop provider 630 provides neurofeedbackor peripheral feedback to subject 603 and is either a real-time on-lineor alternatively off-line facilitator of stimuli wherein stimuli may befurther provided or adjusted based on responses from testing subject603. In one embodiment feedback loop provider 630 includes virtualreality technology, wherein the subject 603 may be provided withmulti-sensory input either for diagnosis, treatment or both.

An example of use of a system 600 including a feedback loop provider isas follows. The subject 12 may be asked to perform a particular task. Ifhe is unsuccessful, feedback loop provider 630 receives data showingthat the task was not successfully performed. Feedback loop provider 630may then introduce multi-sensory stimulation either simulating the taskto be performed or a similar task. Testing subject 603 may then be askedagain to perform the particular task. If he is unsuccessful, the sameinputs may be used again. If he is partially successful, either the sameor new inputs may be used to encourage further performance of the task.In this way, the neurological or psychiatric function of the brain maybe restored or enhanced in certain cases, or may be compensated for byactivating other areas of the brain.

Sensory input by feedback loop provider 630 may include, for example,visual input 640, somatosensory input 650, auditory input 660 or anyother sensory input that may aid in restoration of neurologicalactivity. In some embodiments, one type of sensory input is used. Inother embodiments, multiple sensory inputs are provided simultaneouslyor sequentially. It should be readily apparent that by observing theactual flow and by correlating the flow to particular activities orpathologies, the feedback loop can be greatly facilitated.

A system such as the one described can potentially be used for manyneurological and psychiatric conditions such as rehabilitation of braininjuries, treatment of neurocognitive dysfunctions and treatment ofbehavioral and emotional pathologies and problems. It should be notedthat non-clinical applications are also ample, such as analysis ofdecision making, analysis of mood, analysis of personality and ingeneral analysis of any behavioral function.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims. All publications, patents and patentapplications mentioned in this specification are herein incorporated intheir entirety by reference into the specification, to the same extentas if each individual publication, patent or patent application wasspecifically and individually indicated to be incorporated herein byreference. In addition, citation or identification of any reference inthis application shall not be construed as an admission that suchreference is available as prior art to the present invention.

While certain features of the present invention have been illustratedand described herein, many modifications, substitutions, changes, andequivalents may occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the present invention.

What is claimed is:
 1. A method of constructing a flow pattern database,comprising: obtaining EEG and/or MEG signals from multiple subjects fromone or more research groups for a behavioral process; identifying brainactivity patterns for said behavioral process for said research groupsusing a counting method or method of calculating statisticalsignificances of pairs; identifying a plurality of candidate pathwaysfor said brain activity patterns; for each research group, defining aset of flow patterns among functional brain regions based on saidcandidate pathways, thereby constructing the flow pattern database. 2.The method of claim 1, further comprising, prior to said definition ofsaid set of flow patterns, ranking said candidate pathways based onlikelihood for said behavioral process, and reducing the number ofcandidate pathways based on said ranking.
 3. The method of claim 2,being performed iteratively wherein said reducing is based, at least inpart, on said flow pattern database.
 4. The method of claim 2, whereinsaid reducing is based, at least in part, on at least one temporalconstraint.
 5. The method of claim 1, wherein said defining said set offlow patterns comprises using a region matrix.
 6. The method of claim 1,further comprising localizing sources of activity participating in saidbehavioral process, wherein said identification of said plurality ofcandidate pathways is based also on said sources of activity.
 7. Themethod of claim 1, wherein said obtaining said signals from multiplesubjects comprises administering one or more stimuli to said subjects.8. The method of claim 1, wherein said behavioral process comprisesspontaneous activity or a pathological process.
 9. The method of claim1, wherein said signals comprise EEG signals measured using a pluralityof EEG electrodes, and the method comprises analyzing waveforms ofvarying frequencies and amplitudes over time for each EEG electrode. 10.The method of claim 6, wherein said source localization is done prior tosaid identification of said brain activity patterns.
 11. The method ofclaim 6, wherein said identification of said brain activity patterns isdone prior to said source localization.
 12. The method of claim 6, beingperformed iteratively wherein said wherein said source localization isbased, at least in part, on said flow pattern database.